View Item 

Show simple item record

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

dc.contributor.authorRoser, Anna
dc.contributor.authorEnterkine, Josh
dc.contributor.authorRequena-Mullor, Juan M.
dc.contributor.authorGlenn, Nancy F.
dc.contributor.authorBoehm, Alex
dc.contributor.authorDe Graaff, Marie-Anne
dc.contributor.authorClark, Patrick E.
dc.contributor.authorPierson, Fred
dc.contributor.authorCaughlin, T. Trevor
dc.date.accessioned2023-01-11T16:22:42Z
dc.date.available2024-01-11 11:22:39en
dc.date.available2023-01-11T16:22:42Z
dc.date.issued2022-12
dc.identifier.citationRoser, Anna; Enterkine, Josh; Requena-Mullor, Juan M. ; Glenn, Nancy F.; Boehm, Alex; De Graaff, Marie-Anne ; Clark, Patrick E.; Pierson, Fred; Caughlin, T. Trevor (2022). "Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient." Ecosphere 13(12): n/a-n/a.
dc.identifier.issn2150-8925
dc.identifier.issn2150-8925
dc.identifier.urihttps://hdl.handle.net/2027.42/175421
dc.description.abstractThe structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape-scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation (~230–1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf-level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site-specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherfractional photosynthetic cover
dc.subject.otherremote sensing
dc.subject.otherunoccupied aerial vehicles
dc.titleDrone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelEcology and Evolutionary Biology
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/175421/1/ecs24330_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/175421/2/ecs24330.pdf
dc.identifier.doi10.1002/ecs2.4330
dc.identifier.sourceEcosphere
dc.identifier.citedreferenceXiao, J., F. Chevallier, C. Gomez, L. Guanter, J. A. Hicke, A. R. Huete, K. Ichii, et al. 2019. “ Remote Sensing of the Terrestrial Carbon Cycle: A Review of Advances over 50 Years.” Remote Sensing of Environment 233: 111383.
dc.identifier.citedreferenceKattenborn, T., F. E. Fassnacht, and S. Schmidtlein. 2019. “ Differentiating Plant Functional Types Using Reflectance: Which Traits Make the Difference? ” Remote Sensing in Ecology and Conservation 5: 5 – 19.
dc.identifier.citedreferenceKitzes, J., R. Blake, S. Bombaci, M. Chapman, S. M. Duran, T. Huang, M. B. Joseph, et al. 2021. “ Expanding NEON Biodiversity Surveys with New Instrumentation and Machine Learning Approaches.” Ecosphere 12: e03795.
dc.identifier.citedreferenceKoh, L. P., and S. A. Wich. 2012. “ Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation.” Tropical Conservation Science 5: 121 – 32.
dc.identifier.citedreferenceKoutroulis, A. G. 2019. “ Dryland Changes under Different Levels of Global Warming.” Science of the Total Environment 655: 482 – 511.
dc.identifier.citedreferenceLarrue, S., R. Paris, and S. Etienne. 2020. “ The Use of Vascular Plant Densities to Estimate the Age of Undated Lava Flows in Semi-Arid Areas of Fogo Island (Cape Verde, Atlantic Ocean).” Journal of Arid Environments 173: 104042.
dc.identifier.citedreferenceLehnert, L. W., H. Meyer, Y. Wang, G. Miehe, B. Thies, C. Reudenbach, and J. Bendix. 2015. “ Retrieval of Grassland Plant Coverage on the Tibetan Plateau Based on a Multi-Scale, Multi-Sensor and Multi-Method Approach.” Remote Sensing of Environment 164: 197 – 207.
dc.identifier.citedreferenceLu, M., B. Chen, X. Liao, T. Yue, H. Yue, S. Ren, X. Li, Z. Nie, and B. Xu. 2017. “ Forest Types Classification Based on Multi-Source Data Fusion.” Remote Sensing 9: 1153.
dc.identifier.citedreferenceMarvin, D. C., L. P. Koh, A. J. Lynam, S. Wich, A. B. Davies, R. Krishnamurthy, E. Stokes, R. Starkey, and G. P. Asner. 2016. “ Integrating Technologies for Scalable Ecology and Conservation.” Global Ecology and Conservation 7: 262 – 75.
dc.identifier.citedreferenceMcFeeters, S. K. 1996. “ The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17: 1425 – 32.
dc.identifier.citedreferenceNing, Z., X. Zhao, Y. Li, L. Wang, J. Lian, H. Yang, and Y. Li. 2021. “ Plant Community C:N:P Stoichiometry Is Mediated by Soil Nutrients and Plant Functional Groups during Grassland Desertification.” Ecological Engineering 162: 106179.
dc.identifier.citedreferenceOchoa-Hueso, R., D. J. Eldridge, M. Delgado-Baquerizo, S. Soliveres, M. A. Bowker, N. Gross, Y. Le Bagousse-Pinguet, et al. 2018. “ Soil Fungal Abundance and Plant Functional Traits Drive Fertile Island Formation in Global Drylands.” Journal of Ecology 106: 242 – 53.
dc.identifier.citedreferenceOlsoy, P. J., L. A. Shipley, J. L. Rachlow, J. S. Forbey, N. F. Glenn, M. A. Burgess, and D. H. Thornton. 2018. “ Unmanned Aerial Systems Measure Structural Habitat Features for Wildlife across Multiple Scales.” Methods in Ecology and Evolution 9: 594 – 604.
dc.identifier.citedreferencePandit, K., H. Dashti, N. F. Glenn, A. N. Flores, K. C. Maguire, D. J. Shinneman, G. N. Flerchinger, and A. W. Fellows. 2018. “ Optimizing Shrub Parameters to Estimate Gross Primary Production of the Sagebrush Ecosystem Using the Ecosystem Demography (EDv2. 2) Model.” Geoscientific Model Development Discussions: 1 – 23. https://doi.org/10.5194/gmd-2018-264.
dc.identifier.citedreferencePhilip, S. Y., S. F. Kew, G. J. Van Oldenborgh, W. Yang, G. A. Vecchi, F. S. Anslow, S. Li, et al. 2021. “ Rapid Attribution Analysis of the Extraordinary Heatwave on the Pacific Coast of the US and Canada June 2021.” https://www.worldweatherattribution.org/wp-content/uploads/NW-US-extreme-heat-2021-scientific-report-WWA.pdf.
dc.identifier.citedreferencePolley, H. W., C. A. Kolodziejczyk, K. A. Jones, J. D. Derner, D. J. Augustine, and D. R. Smith. 2022. “ UAV−Enabled Quantification of Grazing-Induced Changes in Uniformity of Green Cover on Semiarid and Mesic Grasslands.” Rangeland Ecology & Management 80: 68 – 77.
dc.identifier.citedreferencePoulter, B., D. Frank, P. Ciais, R. B. Myneni, N. Andela, J. Bi, G. Broquet, et al. 2014. “ Contribution of Semi-Arid Ecosystems to Interannual Variability of the Global Carbon Cycle.” Nature 509: 600 – 3.
dc.identifier.citedreferenceQi, J., A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian. 1994. “ A Modified Soil Adjusted Vegetation Index.” Remote Sensing of Environment 48: 119 – 26.
dc.identifier.citedreferenceRenne, R. R., D. R. Schlaepfer, K. A. Palmquist, J. B. Bradford, I. C. Burke, and W. K. Lauenroth. 2019. “ Soil and Stand Structure Explain Shrub Mortality Patterns Following Global Change–Type Drought and Extreme Precipitation.” Ecology 100: 1 – 17.
dc.identifier.citedreferenceRominger, K., and S. E. Meyer. 2019. “ Application of UAV-Based Methodology for Census of an Endangered Plant Species in a Fragile Habitat.” Remote Sensing 11: 719.
dc.identifier.citedreferenceRondeaux, G., M. Steven, and F. Baret. 1996. “ Optimization of Soil-Adjusted Vegetation Indices.” Remote Sensing of Environment 55: 95 – 107.
dc.identifier.citedreferenceRouse Jr, J. W., R. H. Haas, D. W. Deering, J. A. Schell, and J. C. Harlan. 1974. “ Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation.” https://ntrs.nasa.gov/citations/19750020419.
dc.identifier.citedreferenceShriver, R. K., C. M. Andrews, R. S. Arkle, D. M. Barnard, M. C. Duniway, M. J. Germino, D. S. Pilliod, D. A. Pyke, J. L. Welty, and J. B. Bradford. 2019. “ Transient Population Dynamics Impede Restoration and May Promote Ecosystem Transformation after Disturbance.” Ecology Letters 22: 1357 – 66.
dc.identifier.citedreferenceVivoni, E. R., E. R. Pérez-Ruiz, Z. T. Keller, E. A. Escoto, R. C. Templeton, N. P. Templeton, C. A. Anderson, et al. 2021. “ Long-Term Research Catchments to Investigate Shrub Encroachment in the Sonoran and Chihuahuan Deserts: Santa Rita and Jornada Experimental Ranges.” Hydrological Processes 35: e14031.
dc.identifier.citedreferenceWainwright, C. E., G. M. Davies, E. Dettweiler-Robinson, P. W. Dunwiddie, D. Wilderman, and J. D. Bakker. 2019. “ Methods for Tracking Sagebrush-Steppe Community Trajectories and Quantifying Resilience in Relation to Disturbance and Restoration.” Restoration Ecology 28: 115 – 26.
dc.identifier.citedreferenceWood, D. J. A., T. M. Preston, S. Powell, and P. C. Stoy. 2022. “ Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups.” Remote Sensing 14: 1290.
dc.identifier.citedreferenceWoodward, F. I., and W. Cramer. 1996. “ Plant Functional Types and Climatic Change: Introduction.” Journal of Vegetation Science 7: 306 – 8.
dc.identifier.citedreferenceBannari, A., D. Morin, F. Bonn, and A. R. Huete. 1995. “ A Review of Vegetation Indices.” Remote Sensing Reviews 13: 95 – 120.
dc.identifier.citedreferenceBaret, F., and G. Guyot. 1991. “ Potentials and Limits of Vegetation Indices for LAI and APAR Assessment.” Remote Sensing of Environment 35: 161 – 73.
dc.identifier.citedreferenceBeckstead, J., S. E. Meyer, B. M. Connolly, M. B. Huck, and L. E. Street. 2010. “ Cheatgrass Facilitates Spillover of a Seed Bank Pathogen onto Native Grass Species: Indirect Influence of Seed Bank Pathogen Loads.” Journal of Ecology 98: 168 – 77.
dc.identifier.citedreferenceBickel, J. E. 2007. “ Some Comparisons among Quadratic, Spherical, and Logarithmic Scoring Rules.” Decision Analysis 4: 49 – 65.
dc.identifier.citedreferenceBrantley, S. L., W. H. McDowell, W. E. Dietrich, T. S. White, P. Kumar, S. P. Anderson, J. Chorover, et al. 2017. “ Designing a Network of Critical Zone Observatories to Explore the Living Skin of the Terrestrial Earth.” Earth Surface Dynamics 5: 841 – 60.
dc.identifier.citedreferenceBreulmann, M., E. Schulz, K. Weißhuhn, and F. Buscot. 2012. “ Impact of the Plant Community Composition on Labile Soil Organic Carbon, Soil Microbial Activity and Community Structure in Semi-Natural Grassland Ecosystems of Different Productivity.” Plant and Soil 352: 253 – 65.
dc.identifier.citedreferenceCaughlin, T. T., C. Barber, G. P. Asner, N. F. Glenn, S. A. Bohlman, and C. H. Wilson. 2021. “ Monitoring Tropical Forest Succession at Landscape Scales despite Uncertainty in Landsat Time Series.” Ecological Applications 31: e02208.
dc.identifier.citedreferenceChen, B., B. Huang, and B. Xu. 2017. “ Multi-Source Remotely Sensed Data Fusion for Improving Land Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 124: 27 – 39.
dc.identifier.citedreferenceChen, J., S. Yi, Y. Qin, and X. Wang. 2016. “ Improving Estimates of Fractional Vegetation Cover Based on UAV in Alpine Grassland on the Qinghai–Tibetan Plateau.” International Journal of Remote Sensing 37: 1922 – 36.
dc.identifier.citedreferenceChen, N., K. Yu, R. Jia, J. Teng, and C. Zhao. 2020. “ Biocrust as One of Multiple Stable States in Global Drylands.” Science Advances 6: eaay3763.
dc.identifier.citedreferenceClark, P. 2022a. “Data from: UAS Imagery Protocols to Map Vegetation Are Transferable between Dryland Sites across an Elevational Gradient.” Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1527856.
dc.identifier.citedreferenceClark, P. 2022b. “UAS Imagery Protocols to Map Vegetation Are Transferable between Dryland Sites across an Elevational Gradient.” Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1528092.
dc.identifier.citedreferenceClark, P. E., and M. S. Seyfried. 2001. “ Point Sampling for Leaf Area Index in Sagebrush Steppe Communities.” Journal of Range Management 54: 589.
dc.identifier.citedreferenceCunliffe, A. M., R. E. Brazier, and K. Anderson. 2016. “ Ultra-Fine Grain Landscape-Scale Quantification of Dryland Vegetation Structure with Drone-Acquired Structure-from-Motion Photogrammetry.” Remote Sensing of Environment 183: 129 – 43.
dc.identifier.citedreferenceDahlin, K. M., G. P. Asner, and C. B. Field. 2014. “ Linking Vegetation Patterns to Environmental Gradients and Human Impacts in a Mediterranean-Type Island Ecosystem.” Landscape Ecology 29: 1571 – 85.
dc.identifier.citedreferenceDashti, H., A. Poley, N. F. Glenn, N. Ilangakoon, L. Spaete, D. Roberts, J. Enterkine, A. N. Flores, S. L. Ustin, and J. J. Mitchell. 2019. “ Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach.” Remote Sensing 11: 2141.
dc.identifier.citedreferenceDudley, K. L., P. E. Dennison, K. L. Roth, D. A. Roberts, and A. R. Coates. 2015. “ A Multi-Temporal Spectral Library Approach for Mapping Vegetation Species across Spatial and Temporal Phenological Gradients.” Remote Sensing of Environment 167: 121 – 34.
dc.identifier.citedreferenceFisk, C., K. Clarke, S. Delean, and M. Lewis. 2019. “ Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare? ” Remote Sensing 11: 2589.
dc.identifier.citedreferenceFraser, R. H., I. Olthof, T. C. Lantz, C. Schmitt, R. H. Fraser, I. Olthof, and C. Schmitt. 2016. “ UAV Photogrammetry for Mapping Vegetation in the Low-Arctic.” Arctic Science 2: 79 – 102.
dc.identifier.citedreferenceGillan, J. K., J. W. Karl, M. Duniway, and A. Elaksher. 2014. “ Modeling Vegetation Heights from High Resolution Stereo Aerial Photography: An Application for Broad-Scale Rangeland Monitoring.” Journal of Environmental Management 144: 226 – 35.
dc.identifier.citedreferenceGillan, J. K., J. W. Karl, and W. J. D. van Leeuwen. 2020. “ Integrating Drone Imagery with Existing Rangeland Monitoring Programs.” Environmental Monitoring and Assessment 192: 269.
dc.identifier.citedreferenceGitelson, A. A., and M. N. Merzlyak. 1998. “ Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves.” Advances in Space Research 22: 689 – 92.
dc.identifier.citedreferenceGriebel, A., D. Metzen, M. M. Boer, C. V. M. Barton, A. A. Renchon, H. M. Andrews, and E. Pendall. 2020. “ Using a Paired Tower Approach and Remote Sensing to Assess Carbon Sequestration and Energy Distribution in a Heterogeneous Sclerophyll Forest.” Science of the Total Environment 699: 133918.
dc.identifier.citedreferenceHavrilla, C. A., M. L. Villarreal, J. L. DiBiase, M. C. Duniway, and N. N. Barger. 2020. “ Ultra-High-Resolution Mapping of Biocrusts with Unmanned Aerial Systems.” Remote Sensing in Ecology and Conservation 6: 441 – 56.
dc.identifier.citedreferenceHossain, M. D., and D. Chen. 2019. “ Segmentation for Object-Based Image Analysis (OBIA): A Review of Algorithms and Challenges from Remote Sensing Perspective.” ISPRS Journal of Photogrammetry and Remote Sensing 150: 115 – 34.
dc.identifier.citedreferenceHowell, R. G., R. R. Jensen, S. L. Petersen, and R. T. Larsen. 2020. “ Measuring Height Characteristics of Sagebrush ( Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS).” Drones 4: 6.
dc.identifier.citedreferenceHudon, S. F., A. Zaiats, A. Roser, A. Roopsind, C. Barber, B. C. Robb, B. A. Pendleton, et al. 2021. “ Unifying Community Detection across Scales from Genomes to Landscapes.” Oikos 130: 831 – 43.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
ecs24330_am.pdf
Size:
4.681MB
Format:
PDF
View/Open
Name:
ecs24330.pdf
Size:
7.006MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.